Metadata-Version: 2.2
Name: PyDepManger
Version: 0.1.1
Home-page: https://github.com/abuawadd/PyDepManger
Author: Example Author1, Another Author2
Author-email: author2@example.com, another2@example.com
Maintainer: John, Jane
Maintainer-email: john@example.com, jane@example.com
Project-URL: Documentation, https://github.com/abuawadd/PyDepManger/wiki
Project-URL: Source, https://github.com/abuawadd/PyDepManger
Project-URL: Changelog, https://github.com/abuawadd/PyDepManger/blob/main/CHANGELOG.md
Keywords: example,demo,setuptools,package
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: C
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: pandas==2.2.3
Requires-Dist: numpy==2.2.1
Requires-Dist: mlxtend==0.23.3
Requires-Dist: scikit-learn==1.6.1
Requires-Dist: scipy==1.15.0
Requires-Dist: statsmodels==0.14.4
Requires-Dist: joblib==1.4.2
Requires-Dist: tqdm==4.67.1
Requires-Dist: pyarrow==18.1.0
Requires-Dist: pyspark==3.5.4
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: maintainer
Dynamic: maintainer-email
Dynamic: project-url
Dynamic: requires-dist
Dynamic: requires-python

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        <img src="https://github.com/AbuAwadM/PyDepManger/blob/main/Picture1.png?raw=true" alt="Mode Image">
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---
<!-- # PyHLicorn -->
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/PyDepManger)
![PyPI - Version](https://img.shields.io/pypi/v/PyDepManger)
![GitHub repo size](https://img.shields.io/github/repo-size/AbuAwadM/PyDepManger)


A Python package that re-implements an existing deprecated R package.

- **[Documentation](https://www.google.co.uk/)**
- **[Source Code](https://www.google.co.uk/)**
- **[Research Paper](https://www.google.co.uk/)**
- **[Issues](https://www.google.co.uk/)**

The PyHLicorn package aims to infer a large-scale transcription co-regulatory network from transcriptomic data and integrate external data on gene regulation to infer and analyze transcriptional programs. The unique aspect of the network inference algorithm proposed in the package is its ability to learn co-regulation networks where gene regulation is modeled by transcription factors acting cooperatively to synergistically regulate target genes.

## About the Project
The package was utilized in a study of Bladder Cancer to identify the driver transcriptional programs from a set of 183 samples. Throughout this vignette, a smaller version of the transcriptomic dataset is used to illustrate the package's usage.

## Installation

To install the package, use pip:
```sh
pip install PyHLicorn
```

To clone the repository, use GitHub:
```sh
git clone https://www.google.co.uk/
```

## Usage
Refer to the [documentation](https://www.google.co.uk/) for detailed usage instructions.

Here is an example of how to use the PyHLicorn package in your Python code:

### Import the Library
```python
import pandas as pd
from PyHLicorn import HLicorn
```

### Import the Data
```python
numerical_expression = pd.read_csv(file_path, index_col=0)
discrete_expression = pd.read_csv(file_path, index_col=0)
tf_list = pd.read_csv(file_path, index_col=0)
```

### Create the Gene Regulatory Network
```python
GRN = HLicorn(numerical_expression, tf_list, discrete_expression)
```
## Authors

- **John Doe** - *Initial work* - [JohnDoe]()
- **Jane Smith** - *Contributor* - [JaneSmith]()

## Maintainers

- **Alice Johnson** - [AliceJohnson]()
- **Bob Brown** - [BobBrown]()

## Credits

- Special thanks to the [XYZ Lab](https://www.xyzlab.com) for their support and resources.
- Thanks to all contributors who have helped improve this project.

## Citation

If you use this package in your research, please cite the following paper:

```
@article{Doe2023,
                                title={PyHLicorn: A Python package for transcription co-regulatory network inference},
                                author={Doe, John and Smith, Jane},
                                journal={Journal of Computational Biology},
                                volume={30},
                                number={4},
                                pages={123-134},
                                year={2023},
                                publisher={Bioinformatics Press}
}
```
